Assessment of blood glucose predictors: The prediction-error grid analysis

Diabetes Technology and Therapeutics

Volumn 13, Issue 8, 2011, Pages 787-796

  Sivananthan, Sampath ^a   Naumova, Valeriya ^a   Man, Chiara Dalla ^b   Facchinetti, Andrea ^b   Renard, Eric ^c,d   Cobelli, Claudio ^b   Pereverzyev, Sergei V ^a  

^a JOHANN RADON INSTITUTE FOR COMPUTATIONAL AND APPLIED MATHEMATICS   (Austria)

^b UNIVERSITY OF PADOVA   (Italy)

^c MONTPELLIER UNIVERSITY HOSPITAL   (France)

^d CNRS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTIC METHOD; ANALYTICAL PARAMETERS; ARTICLE; BLOOD GLUCOSE MONITORING; CONTINOUS GLUCOSE ERROR GRID ANALYSIS; CONTROLLED STUDY; DATA ANALYSIS; GLUCOSE BLOOD LEVEL; GOLD STANDARD; HUMAN; HYPERGLYCEMIA; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; PREDICTION ERROR GRID ANALYSIS; PREDICTIVE VALUE; PRIORITY JOURNAL; SIMULATION;

ADOLESCENT; AGED; BLOOD GLUCOSE; BLOOD GLUCOSE SELF-MONITORING; DIABETES MELLITUS, TYPE 1; FORECASTING; HUMANS; MIDDLE AGED; YOUNG ADULT;

EID: 79960434320     PISSN: 15209156     EISSN: 15578593     Source Type: Journal    
DOI: 10.1089/dia.2011.0033     Document Type: Article

References (18)

1. Palerm CC, Bequette BW: Hypoglycemia detection and prediction using continuous glucose monitoring-a study on hypoglycemic clamp data. J Diabetes Sci Technol 2007;1:624-629.
2. Buckingham B, Chase HP, Dassau E, Cobry E, Clinton P, Gage V, Caswell K, Wilkinson J, Cameron F, Lee H, Bequette BW, Doyle FJ III: Prevention of nocturnal hypoglycemia using predictive alarm algorithms and insulin pump suspension. Diabetes Care 2010;33:1013-1018.
3. Bequette BW: Continuous glucose monitoring: real-time algorithms for calibration, filtering and alarms. J Diabetes Sci Technol 2010;4:404-418.
4. Sparacino G, Zanderigo F, Corazza S, Maran A: Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series. IEEE Trans Biomed Eng 2007;54:931-937. (Pubitemid 46628560)
5. Reifman J, Rajaraman S, Gribok A, Kenneth Ward W: Predictive monitoring for improved management of glucose levels. J Diabetes Sci Technol 2007;1:478-486.
6. Pappada SM, Cameron BD, Rosman PM: Development of neural network for prediction of glucose concentration in Type 1 diabetes patients. J Diabetes Sci Technol 2008;2: 792-801.
7. Eren M, Cinar A, Quinn L, Smith D: Estimation of future glucose concentrations with subject-specific recursive linear models. Diabetes Technol Ther 2009;11:243-253.
8. Zanderigo F, Sparacino G, Kovatchev B, Cobelli C: Glucose prediction algorithms from continuous monitoring data: assessment of accuracy via continuous glucose error-grid analysis. J Diabetes Sci Technol 2007;1:645-651.
9. Kovatchev BP, Gonder-Frederick LA, Cox DJ, Clarke WL: Evaluating the accuracy of continuous glucose-monitoring sensors: continuous glucose-error grid analysis illustrated by TheraSense Freestyle Navigator. Diabetes Care 2004;27: 1922-1928. (Pubitemid 38989233)
10. Clarke WL: The original Clarke error grid analysis (EGA). Diabetes Technol Ther 2005;7:776-779. (Pubitemid 41572081)
11. Kovatchev BP, Clarke WL: Peculiarities of the continuous glucose monitoring data stream and their impact on developing closed-loop control technology. J Diabetes Sci Technol 2008;2:158-163.
12. Kovatchev BP, Breton MD, Dalla Man C, Cobelli C: In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes. J Diabetes Sci Technol 2009;3:44-55.
13. De Vito E, Pereverzev SV, Rosasco L: Adaptive kernel methods using the balancing principle. Found Comput Math 2010;10:455-479.
14. Pereverzev SV, Sivananthan S. Regularized learning algorithm for prediction of blood glucose concentration in "No Action Period." In: Nithiarasu P, Lohner R, Van Loon R, eds. In: 1st International Conference onMathematical andComputational Biomedical Engineering. Swansea, UK: CMBE, 2009:395-398.
15. Poulsen JU, Avogaro A, Chauchard F, Cobelli C, Johansson R, Nita L, Pogose M, del Re L, Renard E, Sivananthan S, Saudek F, Skillen M, Soendergaard J: A diabetes management system empowering patients to reach optimised glucose control: from monitor to advisor. In: 32nd Annual International IEEE EMBS Conference. Buenos Aires: EMBC, 2010, Paper FrE14.3.
16. Facchinetti A, Sparacino G, Trifoglio E, Cobelli C: A new index to optimally design and compare CGM glucose prediction algorithms. Diabetes Technol Ther 2011;13:111-119.
17. Lu S, Pereverzev SV:Numerical differentiation from a viewpoint of regularization theory. Math Comput 2006;75:1853-1870. (Pubitemid 44719734)
18. MathéP, Pereverzev SV: The use of higher order finite difference schemes is not dangerous. J Complexity 2009;25:3-10.